Abstract. Atmospheric carbon dioxide levels can be mitigated by sequestering carbon in the soil. Sequestration can be facilitated by agricultural management, but its influence is not the same on all soil carbon pools, as labile pools with a high turnover may be accumulated much faster but are also more vulnerable to losses. The aims of this study were to (1) assess how soil organic carbon (SOC) is distributed among SOC fractions on a national scale in Germany, (2) identify factors influencing this distribution and (3) identify regions with high vulnerability to SOC losses. The SOC content and proportion of two different SOC fractions were estimated for more than 2500 mineral topsoils (< 87 g kg−1 SOC) covering Germany, using near-infrared reflectance spectroscopy. Drivers of the spatial variability in SOC fractions were determined using the machine learning algorithm cforest. The SOC content and proportions of fractions were predicted with good accuracy (SOC content: R2 = 0.87–0.90; SOC proportions: R2 = 0.83; ratio of performance to deviation (RPD): 2.4–3.2). The main explanatory variables for the distribution of SOC among the fractions were soil texture, bulk soil C ∕ N ratio, total SOC content and pH. For some regions, the drivers were linked to the land-use history of the sites. Arable topsoils in central and southern Germany were found to contain the highest proportions and contents of stable SOC fractions, and therefore have the lowest vulnerability to SOC losses. North-western Germany contains an area of sandy soils with unusually high SOC contents and high proportions of light SOC fractions, which are commonly regarded as representing a labile carbon pool. This is true for the former peat soils in this area, which have already lost and are at high risk of losing high proportions of their SOC stocks. Those “black sands” can, however, also contain high amounts of stable SOC due to former heathland vegetation and need to be treated and discussed separately from non-black sand agricultural soils. Overall, it was estimated that, in large areas all over Germany, over 30 % of SOC is stored in easily mineralisable forms. Thus, SOC-conserving management of arable soils in these regions is of great importance.
Abstract. Atmospheric carbon dioxide levels can be mitigated by sequestering carbon in the soil. Sequestration can be facilitated by agricultural management, but its influence is not the same on all soil carbon pools, as labile pools with high turnover may be accumulated much faster, but are also more vulnerable to losses. The aims of this study were to (1) assess how soil organic carbon (SOC) is distributed among SOC fractions on national scale in Germany, (2) identify factors influencing this distribution and (3) identify regions with high vulnerability to SOC losses. The SOC content and proportion of two different SOC fractions were estimated for more than 2500 mineral topsoils (
Summary Research has shown that the application of near‐infrared (NIR) spectroscopy can be used to predict soil attributes, in particular for regional to continental scales. However, there are challenges when NIR is used at the regional scale because of the considerable spatial variation. This study has predicted SOC at the country scale (German agricultural soil inventory) with different stratification strategies for NIR data: (i) calibration with memory‐based learning (MBL) algorithms that use spectral similarity and (ii) simple stratification based on soil properties (depth, pH and soil texture) and land use. To optimize calibration models, this study aimed to predict soil organic carbon (SOC) determined by these three strategies for 1410 soil profiles selected from the German agricultural soil inventory. The profiles covered a wide range of soil types and characteristics. The calibration procedures were based on complete soil profile data of two‐thirds of the dataset and one‐third of the dataset was used for independent validation (prediction); the profiles were selected randomly. Available soil properties for stratifying the datasets were: soil depth (topsoil 0–30 cm and subsoil 31–100 cm), pH and texture class (silty, clayey, sandy and loamy). The profiles were also stratified by land use (cropland and grassland) and with the MBL method. The calibrations were carried out by partial least‐squares regression (PLSR), and each stratification model was compared with the global model. The root mean square error of cross‐validation (RMSECV) for the global model was 4.2 g SOC kg−1. Stratification according to soil depth reduced the error by 10% (RMSECV 3.8 g kg−1). The best stratification by soil texture was when sandy soil samples were separated from the other samples, which reduced the RMSECV by 14%. Calibration with MBL provided the most accurate predictions of SOC, with an error reduction of 25% (RMSECV 3.2 g kg−1). Thus, calibrations with NIR of country‐scale datasets can be improved easily by stratification or application of the MBL algorithm. Highlights Large country‐scale soil dataset of near‐infrared with > 1400 soil profiles was used. Stratification of NIR data by soil properties increased the accuracy of SOC calibration at country scale. The best stratification design involved calibrating sandy soil separately from other texture classes. A decrease of 25% in calibration error and 22% in prediction error with the MBL model compared with global model.
Summary Information about soil organic carbon fractions is important in understanding the vulnerability of soil carbon to climate change and land management. Soil organic carbon can be divided into fractions that are labile and others that are more stable. All existing methods to fractionate soil organic carbon are time consuming and complex. Near‐infrared reflectance spectroscopy (NIRS) is a rapid analytical technique. In this study we evaluated and optimized the use of NIRS to predict soil organic carbon fractions with the constraint that the carbon fractions (labile and stabilized) should add up to 100% (total organic carbon content). We used samples from two datasets from agricultural and forest sites in Germany (dataset A) and Europe (dataset B). Samples were fractionated by two different methods (density and physical‐chemical fractionation) as reference methods. Soil samples were scanned in the NIR range and calibration models were developed using partial least squares regression. The key to improving model performance was the log‐ratio transformation proposed by Aitchison for compositional data that enabled us to model the fractions as dependent variables. Traditional methods for the optimization of NIRS models, such as selection of wavelength range and pretreatment of spectra, showed no effective reduction in error. With the constraint that both fractions add to 100% (log‐ratio transformation), the ratio of performance to deviation (RPD) increased from 1.6 to 2.8 for the labile C fraction and from 1.5 to 3.2 for the stabilized C fraction. Root mean square error of cross‐validation of the labile fraction was 4.3 g C kg−1 for dataset A and 2.7 g C kg−1 for dataset B, which corresponds to an R2 of 0.88–0.80 and RPD of 2.9–2.2, respectively. The models performed equally well for different soil textures and land‐use types. With log‐ratio transformation, more precise calibrations of NIRS for C fractions could be obtained. Highlights Near‐infrared spectroscopy can predict soil carbon fractions at different spatial extents Log‐ratio transformation satisfies the constraint that fractions should add up to 100% Log‐ratio transformation is the key to improving calibrations for soil C fractions. The NIR models were tested with two fractionation schemes comprising different land uses and soil texture
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.